Easily measure forecast model accuracy to optimize your business objectives with Amazon Forecast
We’re excited to announce that you can now measure the accuracy of your forecasting model to optimize the trade-offs between under-forecasting and over-forecasting costs, giving you flexibility in experimentation. Costs associated with under-forecasting and over-forecasting differ. Amazon Forecast allows you to optimize these costs for your business objective by providing an average forecast as well as a distribution of forecasts that captures variability of demand from a minimum to maximum value. With this launch, Forecast now provides accuracy metrics for multiple distribution points when training a model, allowing you to quickly optimize for under-forecasting and over-forecasting without the need to manually calculate metrics.
We’re excited to announce that you can now measure the accuracy of your forecasting model to optimize the trade-offs between under-forecasting and over-forecasting costs, giving you flexibility in experimentation. Costs associated with under-forecasting and over-forecasting differ. Amazon Forecast allows you to optimize these costs for your business objective by providing an average forecast as well as a distribution of forecasts that captures variability of demand from a minimum to maximum value. With this launch, Forecast now provides accuracy metrics for multiple distribution points when training a model, allowing you to quickly optimize for under-forecasting and over-forecasting without the need to manually calculate metrics.
Retailers rely on probabilistic forecasting to optimize their supply chain to balance the cost of under-forecasting, leading to stock-outs, with the cost of over-forecasting, leading to inventory carrying costs and waste. Depending on the product category, retailers may choose to generate forecasts at different distribution points. For example, grocery retailers choose to over-stock staples such as milk and eggs to meet variable demand. While these staple foods have relatively low carrying costs, stock-outs may not only lead to a lost sale, but also a complete cart abandonment. By maintaining high in-stock rates, retailers can improve customer satisfaction and customer loyalty. Conversely, retailers may choose to under-stock substitutable products with high carrying costs when the cost of markdowns and inventory disposal outweighs the occasional lost sale. The ability to forecast at different distribution points allows retailers to optimize these competing priorities when demand is variable.
Although Forecast provided the ability to forecast at the entire distribution of variability to manage the trade-offs of under-stocking and over-stocking, the accuracy metrics were only provided for the minimum, median, and maximum predicted demand, providing an 80% confidence band centered on the median. To evaluate the accuracy metric at a particular distribution point of interest, you first had to create forecasts at that point, and then manually calculate the accuracy metrics on your own. With today’s launch, you can assess the strengths of your forecasting models at any distribution point within Forecast without needing to generate forecasts and manually calculate metrics. This capability enables you to experiment faster, and more cost-effectively arrive at a distribution point for your business needs.
To use this new capability, learn more about selecting multiple distribution points and understanding model accuracy metrics in our blog. Also refer to the API documentation pages for Evaluating Predictor Accuracy, CreatePredictor API and GetAccuracyMetrics API. You can use this capability in all Regions where Amazon Forecast is publicly available. For more information about Region availability, see Region Table.